Low Light Image Enhancement for Dark Images

Akshay Patil; Tejas Chaudhari; Ketan Deo; Kalpesh Sonawane; Rupali Bora

doi:doi:10.11648/j.ijdsa.20200604.11

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Low Light Image Enhancement for Dark Images

Akshay Patil, Tejas Chaudhari, Ketan Deo, Kalpesh Sonawane, Rupali Bora

Published in International Journal of Data Science and Analysis (Volume 6, Issue 4)

Received: 10 May 2020 Accepted: 25 May 2020 Published: 7 September 2020

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Abstract

Image plays an important role in this present technological world and leads to progress in multimedia communication, various research fields related to image processing, etc. Low-light image enhancement specifically addresses images captured in low-light conditions such as nighttime, where the common goal is to brighten and improve the contrast of the image for better visual quality and show details that are hidden in darkness. Research fields that may assist us in lowlight environments, such as object detection, has glossed over this aspect even though breakthroughs-after breakthroughs had been achieved in recent years, most noticeably from the lack of low-light data (less than 2% of the total images) in successful public benchmark datasets such as PASCAL VOC, ImageNet, and Microsoft COCO. To improve image quality, these low-light images are needed to be enhanced. For this purpose, an exclusively dark dataset comprising of images captured in visible light only is proposed. Further, dehazing technique is used for haze removal, histogram equalization (HE) technique is used for contrast enhancement and denoising technique is used for noise removal. Experimental results demonstrate that the proposed method achieves a good performance in low light image enhancement and outperforms state-of-the-art ones in terms of contrast enhancement and noise reduction.

Published in	International Journal of Data Science and Analysis (Volume 6, Issue 4)
DOI	10.11648/j.ijdsa.20200604.11
Page(s)	99-104
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2020. Published by Science Publishing Group

Keywords

Dataset, Dehazing, Denoising, Enhancement, Histogram Equalization, Low-light

References

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[4]	Y. P. Loh, C. S. Chan, Getting to know low-light images with the exclusively dark dataset, Comput. Vis. Image Understanding 178 (2018) 30–42.
[5]	Everingham, M., Eslami, S. A., Van Gool, L., Williams, C. K., Winn, J., Zisserman, A., 2015. The pascal visual object classes challenge: A retrospective. Int. J. Comput. Vis. 111, 98–136.
[6]	Guo, X., Li, Y., Ling, H., 2017. Lime: Low-light image enhancement via illumination map estimation. IEEE Trans. Image Process. 26, 982–993.
[7]	Lim, J., Kim, J. H., Sim, J. Y., Kim, C. S., 2015. Robust contrast enhancement of noisy lowlight images: Denoising-enhancement-completion. In: Image Processing (ICIP), 2015 IEEE International Conference on. IEEE, pp. 4131–4135.
[8]	Li, L., Wang, R., Wang, W., Gao, W., 2015. A low-light image enhancement method for both denoising and contrast enlarging. In: Image Processing (ICIP), 2015 IEEE International Conference on. IEEE, pp. 3730–3734.
[9]	K. He, J. Sun and X. Tang: Single Image Haze Removal Using Dark Channel Prior, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 33, No. 12, 2011, pp. 2341 − 2353.
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[11]	S. Puzović, R. Petrović, M. Pavlović and S. Stanković, "Enhancement Algorithms for Low-Light and Low-Contrast Images," 2020 19th International Symposium INFOTEH-JAHORINA (INFOTEH), East Sarajevo, Bosnia and Herzegovina, 2020, pp. 1-6, doi: 10.1109/INFOTEH48170.2020.9066316.
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[14]	Leo, M., Medioni, G., Trivedi, M., Kanade, T., Farinella, G. M., 2017. Computer vision for assistive technologies. Comput. Vision Image Understanding 154, 1–15.
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APA Style

Akshay Patil, Tejas Chaudhari, Ketan Deo, Kalpesh Sonawane, Rupali Bora. (2020). Low Light Image Enhancement for Dark Images. International Journal of Data Science and Analysis, 6(4), 99-104. https://doi.org/10.11648/j.ijdsa.20200604.11

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ACS Style

Akshay Patil; Tejas Chaudhari; Ketan Deo; Kalpesh Sonawane; Rupali Bora. Low Light Image Enhancement for Dark Images. Int. J. Data Sci. Anal. 2020, 6(4), 99-104. doi: 10.11648/j.ijdsa.20200604.11

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AMA Style

Akshay Patil, Tejas Chaudhari, Ketan Deo, Kalpesh Sonawane, Rupali Bora. Low Light Image Enhancement for Dark Images. Int J Data Sci Anal. 2020;6(4):99-104. doi: 10.11648/j.ijdsa.20200604.11

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@article{10.11648/j.ijdsa.20200604.11,
  author = {Akshay Patil and Tejas Chaudhari and Ketan Deo and Kalpesh Sonawane and Rupali Bora},
  title = {Low Light Image Enhancement for Dark Images},
  journal = {International Journal of Data Science and Analysis},
  volume = {6},
  number = {4},
  pages = {99-104},
  doi = {10.11648/j.ijdsa.20200604.11},
  url = {https://doi.org/10.11648/j.ijdsa.20200604.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijdsa.20200604.11},
  abstract = {Image plays an important role in this present technological world and leads to progress in multimedia communication, various research fields related to image processing, etc. Low-light image enhancement specifically addresses images captured in low-light conditions such as nighttime, where the common goal is to brighten and improve the contrast of the image for better visual quality and show details that are hidden in darkness. Research fields that may assist us in lowlight environments, such as object detection, has glossed over this aspect even though breakthroughs-after breakthroughs had been achieved in recent years, most noticeably from the lack of low-light data (less than 2% of the total images) in successful public benchmark datasets such as PASCAL VOC, ImageNet, and Microsoft COCO. To improve image quality, these low-light images are needed to be enhanced. For this purpose, an exclusively dark dataset comprising of images captured in visible light only is proposed. Further, dehazing technique is used for haze removal, histogram equalization (HE) technique is used for contrast enhancement and denoising technique is used for noise removal. Experimental results demonstrate that the proposed method achieves a good performance in low light image enhancement and outperforms state-of-the-art ones in terms of contrast enhancement and noise reduction.},
 year = {2020}
}

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TY  - JOUR
T1  - Low Light Image Enhancement for Dark Images
AU  - Akshay Patil
AU  - Tejas Chaudhari
AU  - Ketan Deo
AU  - Kalpesh Sonawane
AU  - Rupali Bora
Y1  - 2020/09/07
PY  - 2020
N1  - https://doi.org/10.11648/j.ijdsa.20200604.11
DO  - 10.11648/j.ijdsa.20200604.11
T2  - International Journal of Data Science and Analysis
JF  - International Journal of Data Science and Analysis
JO  - International Journal of Data Science and Analysis
SP  - 99
EP  - 104
PB  - Science Publishing Group
SN  - 2575-1891
UR  - https://doi.org/10.11648/j.ijdsa.20200604.11
AB  - Image plays an important role in this present technological world and leads to progress in multimedia communication, various research fields related to image processing, etc. Low-light image enhancement specifically addresses images captured in low-light conditions such as nighttime, where the common goal is to brighten and improve the contrast of the image for better visual quality and show details that are hidden in darkness. Research fields that may assist us in lowlight environments, such as object detection, has glossed over this aspect even though breakthroughs-after breakthroughs had been achieved in recent years, most noticeably from the lack of low-light data (less than 2% of the total images) in successful public benchmark datasets such as PASCAL VOC, ImageNet, and Microsoft COCO. To improve image quality, these low-light images are needed to be enhanced. For this purpose, an exclusively dark dataset comprising of images captured in visible light only is proposed. Further, dehazing technique is used for haze removal, histogram equalization (HE) technique is used for contrast enhancement and denoising technique is used for noise removal. Experimental results demonstrate that the proposed method achieves a good performance in low light image enhancement and outperforms state-of-the-art ones in terms of contrast enhancement and noise reduction.
VL  - 6
IS  - 4
ER  -

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Author Information

Akshay Patil

Information Technology, K. K. Wagh Institute of Engineering Education and Research, Nashik, India
Tejas Chaudhari

Information Technology, K. K. Wagh Institute of Engineering Education and Research, Nashik, India
Ketan Deo

Information Technology, K. K. Wagh Institute of Engineering Education and Research, Nashik, India
Kalpesh Sonawane

Information Technology, K. K. Wagh Institute of Engineering Education and Research, Nashik, India
Rupali Bora

Information Technology, K. K. Wagh Institute of Engineering Education and Research, Nashik, India

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Plain Text BibTeX RIS

APA Style

Akshay Patil, Tejas Chaudhari, Ketan Deo, Kalpesh Sonawane, Rupali Bora. (2020). Low Light Image Enhancement for Dark Images. International Journal of Data Science and Analysis, 6(4), 99-104. https://doi.org/10.11648/j.ijdsa.20200604.11

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ACS Style

Akshay Patil; Tejas Chaudhari; Ketan Deo; Kalpesh Sonawane; Rupali Bora. Low Light Image Enhancement for Dark Images. Int. J. Data Sci. Anal. 2020, 6(4), 99-104. doi: 10.11648/j.ijdsa.20200604.11

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AMA Style

Akshay Patil, Tejas Chaudhari, Ketan Deo, Kalpesh Sonawane, Rupali Bora. Low Light Image Enhancement for Dark Images. Int J Data Sci Anal. 2020;6(4):99-104. doi: 10.11648/j.ijdsa.20200604.11

Copy | Download

@article{10.11648/j.ijdsa.20200604.11,
  author = {Akshay Patil and Tejas Chaudhari and Ketan Deo and Kalpesh Sonawane and Rupali Bora},
  title = {Low Light Image Enhancement for Dark Images},
  journal = {International Journal of Data Science and Analysis},
  volume = {6},
  number = {4},
  pages = {99-104},
  doi = {10.11648/j.ijdsa.20200604.11},
  url = {https://doi.org/10.11648/j.ijdsa.20200604.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijdsa.20200604.11},
  abstract = {Image plays an important role in this present technological world and leads to progress in multimedia communication, various research fields related to image processing, etc. Low-light image enhancement specifically addresses images captured in low-light conditions such as nighttime, where the common goal is to brighten and improve the contrast of the image for better visual quality and show details that are hidden in darkness. Research fields that may assist us in lowlight environments, such as object detection, has glossed over this aspect even though breakthroughs-after breakthroughs had been achieved in recent years, most noticeably from the lack of low-light data (less than 2% of the total images) in successful public benchmark datasets such as PASCAL VOC, ImageNet, and Microsoft COCO. To improve image quality, these low-light images are needed to be enhanced. For this purpose, an exclusively dark dataset comprising of images captured in visible light only is proposed. Further, dehazing technique is used for haze removal, histogram equalization (HE) technique is used for contrast enhancement and denoising technique is used for noise removal. Experimental results demonstrate that the proposed method achieves a good performance in low light image enhancement and outperforms state-of-the-art ones in terms of contrast enhancement and noise reduction.},
 year = {2020}
}

Copy | Download

TY  - JOUR
T1  - Low Light Image Enhancement for Dark Images
AU  - Akshay Patil
AU  - Tejas Chaudhari
AU  - Ketan Deo
AU  - Kalpesh Sonawane
AU  - Rupali Bora
Y1  - 2020/09/07
PY  - 2020
N1  - https://doi.org/10.11648/j.ijdsa.20200604.11
DO  - 10.11648/j.ijdsa.20200604.11
T2  - International Journal of Data Science and Analysis
JF  - International Journal of Data Science and Analysis
JO  - International Journal of Data Science and Analysis
SP  - 99
EP  - 104
PB  - Science Publishing Group
SN  - 2575-1891
UR  - https://doi.org/10.11648/j.ijdsa.20200604.11
AB  - Image plays an important role in this present technological world and leads to progress in multimedia communication, various research fields related to image processing, etc. Low-light image enhancement specifically addresses images captured in low-light conditions such as nighttime, where the common goal is to brighten and improve the contrast of the image for better visual quality and show details that are hidden in darkness. Research fields that may assist us in lowlight environments, such as object detection, has glossed over this aspect even though breakthroughs-after breakthroughs had been achieved in recent years, most noticeably from the lack of low-light data (less than 2% of the total images) in successful public benchmark datasets such as PASCAL VOC, ImageNet, and Microsoft COCO. To improve image quality, these low-light images are needed to be enhanced. For this purpose, an exclusively dark dataset comprising of images captured in visible light only is proposed. Further, dehazing technique is used for haze removal, histogram equalization (HE) technique is used for contrast enhancement and denoising technique is used for noise removal. Experimental results demonstrate that the proposed method achieves a good performance in low light image enhancement and outperforms state-of-the-art ones in terms of contrast enhancement and noise reduction.
VL  - 6
IS  - 4
ER  -

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